Crowdsourcing is a computing paradigm where humans are actively involved in a
computing task, especially for tasks that are intrinsically easier for humans than for …

Matching, capturing allocation of items to unit-demand buyers, or tasks to workers, or pairs of
collaborators, is a central problem in economics. Indeed, the growing prevalence of …

Recent work on machine learning has begun to consider issues of fairness. In this paper, we
extend the concept of fairness to recommendation. In particular, we show that in some …

Matching is a classic problem with a rich history and a significant impact, both on the theory
of algorithms and in practice. Recently there has been a surge of interest in the online …

How does a search engine company decide what ads to display with each query so as to
maximize its revenue? This turns out to be a generalization of the online bipartite matching …

The primal–dual method is a powerful algorithmic technique that has proved to be extremely
useful for a wide variety of problems in the area of approximation algorithms for NP-hard …

We consider the problem of a search engine trying to assign a sequence of search keywords
to a set of competing bidders, each with a daily spending limit. The goal is to maximize the …

Motivated by the availability of real-time data on customer characteristics, we consider the
problem of personalizing the assortment of products for each arriving customer. Using actual …

We study the online ad-auctions problem introduced by Mehta et al.[15]. We design a (1−
1/e)-competitive (optimal) algorithm for the problem, which is based on a clean primal-dual …

We give a simple proof that the ranking algorithm of Karp, Vazirani and Vazirani [KVV90] is 1-
1/e competitive for the online bipartite matching problem. The proof is via a randomized …